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<div class="section" id="torch-compile">
<h1>torch.compile<a class="headerlink" href="#torch-compile" title="Permalink to this heading">¶</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="torch.compile">
<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">compile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fullgraph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'inductor'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">disable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#compile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compile" title="Permalink to this definition">¶</a></dt>
<dd><p>Optimizes given model/function using TorchDynamo and specified backend.</p>
<p>Concretely, for every frame executed within the compiled region, we will attempt
to compile it and cache the compiled result on the code object for future
use. A single frame may be compiled multiple times if previous compiled
results are not applicable for subsequent calls (this is called a “guard
failure), you can use TORCH_LOGS=guards to debug these situations.
Multiple compiled results can be associated with a frame up to
<code class="docutils literal notranslate"><span class="pre">torch._dynamo.config.cache_size_limit</span></code>, which defaults to 64; at which
point we will fall back to eager. Note that compile caches are per
<em>code object</em>, not frame; if you dynamically create multiple copies of a
function, they will all share the same code cache.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>model</strong> (<em>Callable</em>) – Module/function to optimize</p></li>
<li><p><strong>fullgraph</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If False (default), torch.compile attempts to discover compileable regions
in the function that it will optimize. If True, then we require that the entire function be
capturable into a single graph. If this is not possible (that is, if there are graph breaks),
then this will raise an error.</p></li>
<li><p><strong>dynamic</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> or </em><em>None</em>) – Use dynamic shape tracing. When this is True, we will up-front attempt
to generate a kernel that is as dynamic as possible to avoid recompilations when
sizes change. This may not always work as some operations/optimizations will
force specialization; use TORCH_LOGS=dynamic to debug overspecialization.
When this is False, we will NEVER generate dynamic kernels, we will always specialize.
By default (None), we automatically detect if dynamism has occurred and compile a more
dynamic kernel upon recompile.</p></li>
<li><p><strong>backend</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> or </em><em>Callable</em>) – <p>backend to be used</p>
<ul>
<li><p>”inductor” is the default backend, which is a good balance between performance and overhead</p></li>
<li><p>Non experimental in-tree backends can be seen with <cite>torch._dynamo.list_backends()</cite></p></li>
<li><p>Experimental or debug in-tree backends can be seen with <cite>torch._dynamo.list_backends(None)</cite></p></li>
<li><p>To register an out-of-tree custom backend: <a class="reference external" href="https://pytorch.org/docs/stable/torch.compiler_custom_backends.html">https://pytorch.org/docs/stable/torch.compiler_custom_backends.html</a></p></li>
</ul>
</p></li>
<li><p><strong>mode</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – <p>Can be either “default”, “reduce-overhead”, “max-autotune” or “max-autotune-no-cudagraphs”</p>
<ul>
<li><p>”default” is the default mode, which is a good balance between performance and overhead</p></li>
<li><p>”reduce-overhead” is a mode that reduces the overhead of python with CUDA graphs,
useful for small batches. Reduction of overhead can come at the cost of more memory
usage, as we will cache the workspace memory required for the invocation so that we
do not have to reallocate it on subsequent runs. Reduction of overhead is not guaranteed
to work; today, we only reduce overhead for CUDA only graphs which do not mutate inputs.
There are other circumstances where CUDA graphs are not applicable; use TORCH_LOG=perf_hints
to debug.</p></li>
<li><p>”max-autotune” is a mode that leverages Triton based matrix multiplications and convolutions
It enables CUDA graphs by default.</p></li>
<li><p>”max-autotune-no-cudagraphs” is a mode similar to “max-autotune” but without CUDA graphs</p></li>
<li><p>To see the exact configs that each mode sets you can call <cite>torch._inductor.list_mode_options()</cite></p></li>
</ul>
</p></li>
<li><p><strong>options</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – <p>A dictionary of options to pass to the backend. Some notable ones to try out are</p>
<ul>
<li><p><cite>epilogue_fusion</cite> which fuses pointwise ops into templates. Requires <cite>max_autotune</cite> to also be set</p></li>
<li><p><cite>max_autotune</cite> which will profile to pick the best matmul configuration</p></li>
<li><p><cite>fallback_random</cite> which is useful when debugging accuracy issues</p></li>
<li><p><cite>shape_padding</cite> which pads matrix shapes to better align loads on GPUs especially for tensor cores</p></li>
<li><p><cite>triton.cudagraphs</cite> which will reduce the overhead of python with CUDA graphs</p></li>
<li><p><cite>trace.enabled</cite> which is the most useful debugging flag to turn on</p></li>
<li><p><cite>trace.graph_diagram</cite> which will show you a picture of your graph after fusion</p></li>
<li><p>For inductor you can see the full list of configs that it supports by calling <cite>torch._inductor.list_options()</cite></p></li>
</ul>
</p></li>
<li><p><strong>disable</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Turn torch.compile() into a no-op for testing</p></li>
</ul>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.12)"><em>Callable</em></a></p>
</dd>
</dl>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@torch</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">options</span><span class="o">=</span><span class="p">{</span><span class="s2">"triton.cudagraphs"</span><span class="p">:</span> <span class="kc">True</span><span class="p">},</span> <span class="n">fullgraph</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">foo</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">+</span> <span class="n">torch</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
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